THRIVE: Threshold Homomorphic encryption based secure and privacy preserving bIometric VErification system

In this paper, we propose a new biometric verification and template protection system which we call the THRIVE system. The system includes novel enrollment and authentication protocols based on threshold homomorphic cryptosystem where the private key is shared between a user and the verifier. In the THRIVE system, only encrypted binary biometric templates are stored in the database and verification is performed via homomorphically randomized templates, thus, original templates are never revealed during the authentication stage. The THRIVE system is designed for the malicious model where the cheating party may arbitrarily deviate from the protocol specification. Since threshold homomorphic encryption scheme is used, a malicious database owner cannot perform decryption on encrypted templates of the users in the database. Therefore, security of the THRIVE system is enhanced using a two-factor authentication scheme involving the user's private key and the biometric data. We prove security and privacy preservation capability of the proposed system in the simulation-based model with no assumption. The proposed system is suitable for applications where the user does not want to reveal her biometrics to the verifier in plain form but she needs to proof her physical presence by using biometrics. The system can be used with any biometric modality and biometric feature extraction scheme whose output templates can be binarized. The overall connection time for the proposed THRIVE system is estimated to be 336 ms on average for 256-bit biohash vectors on a desktop PC running with quad-core 3.2 GHz CPUs at 10 Mbit/s up/down link connection speed. Consequently, the proposed system can be efficiently used in real life applications

State of the Art in Biometric Key Binding and Key Generation Schemes

Direct storage of biometric templates in databases exposes the authentication system and legitimate users to numerous security and privacy challenges. Biometric cryptosystems or template protection schemes are used to overcome the security and privacy challenges associated with the use of biometrics as a means of authentication. This paper presents a review of previous works in biometric key binding and key generation schemes. The review focuses on key binding techniques such as biometric encryption, fuzzy commitment scheme, fuzzy vault and shielding function. Two categories of key generation schemes considered are private template and quantization schemes. The paper also discusses the modes of operations, strengths and weaknesses of various kinds of key-based template protection schemes. The goal is to provide the reader with a clear understanding of the current and emerging trends in key-based biometric cryptosystems

Decodability Attack against the Fuzzy Commitment Scheme with Public Feature Transforms

The fuzzy commitment scheme is a cryptographic primitive that can be used to store biometric templates being encoded as fixed-length feature vectors protected. If multiple related records generated from the same biometric instance can be intercepted, their correspondence can be determined using the decodability attack. In 2011, Kelkboom et al. proposed to pass the feature vectors through a record-specific but public permutation process in order to prevent this attack. In this paper, it is shown that this countermeasure enables another attack also analyzed by Simoens et al. in 2009 which can even ease an adversary to fully break two related records. The attack may only be feasible if the protected feature vectors have a reasonably small Hamming distance; yet, implementations and security analyses must account for this risk. This paper furthermore discusses that by means of a public transformation, the attack cannot be prevented in a binary fuzzy commitment scheme based on linear codes. Fortunately, such transformations can be generated for the non-binary case. In order to still be able to protect binary feature vectors, one may consider to use the improved fuzzy vault scheme by Dodis et al. which may be secured against linkability attacks using observations made by Merkle and Tams

Coding Solutions for the Secure Biometric Storage Problem

The paper studies the problem of securely storing biometric passwords, such as fingerprints and irises. With the help of coding theory Juels and Wattenberg derived in 1999 a scheme where similar input strings will be accepted as the same biometric. In the same time nothing could be learned from the stored data. They called their scheme a "fuzzy commitment scheme". In this paper we will revisit the solution of Juels and Wattenberg and we will provide answers to two important questions: What type of error-correcting codes should be used and what happens if biometric templates are not uniformly distributed, i.e. the biometric data come with redundancy. Answering the first question will lead us to the search for low-rate large-minimum distance error-correcting codes which come with efficient decoding algorithms up to the designed distance. In order to answer the second question we relate the rate required with a quantity connected to the "entropy" of the string, trying to estimate a sort of "capacity", if we want to see a flavor of the converse of Shannon's noisy coding theorem. Finally we deal with side-problems arising in a practical implementation and we propose a possible solution to the main one that seems to have so far prevented real life applications of the fuzzy scheme, as far as we know.Comment: the final version appeared in Proceedings Information Theory Workshop (ITW) 2010, IEEE copyrigh

A New Way for Face Sketch Construction and Detection Using Deep CNN

Traditional hand-drawn face sketches have encountered speed and accuracy issues in the field of forensic science when used in conjunction with contemporary criminal identification technologies. To close this gap, we provide a ground-breaking research article that is built on a stand-alone program that aims to revolutionize the production and identification of composite face sketches. This ground-breaking approach does away with the requirement for forensic artists by enabling users to easily create composite sketches using a drag-and-drop interface. Utilizing the power of deep learning and cloud infrastructure, these generated sketches are seamlessly cross-referenced against an enormous police database to identify suspects quickly and precisely. Our research study offers a dual-pronged approach to combating the rise in criminal activity while using the quick breakthroughs in artificial intelligence. First, we demonstrate how a specific Deep Convolutional Neural Network model transforms sketches of faces into photorealistic photographs. Second, we employ transfer learning for precise suspect identification using the pre-trained VGG-Face model. Utilizing Convolutional Neural Networks, which are famous for their data processing powers and hierarchical feature extraction, is a key component of our strategy. This approach exceeds current methods and boasts an extraordinary average accuracy of 0.98 in identifying people from sketches, providing a crucial tool for strengthening and speeding up forensic investigations. A unique Convolutional Neural Network framework that demonstrates significant improvements over state-of-the-art techniques is also revealed as we dive into the challenging task of matching composite sketches with corresponding digital photos. Our thorough analysis shows the framework to be remarkably accurate, constituting a substantial advance in the field of forensic face sketch production and recognition

Integrated Biometric Template Security using Random Rectangular Hashing

Large centralized biometric databases, accessible over networks in real time are especially used for identification purposes. Multimodal biometric systems which are more robust and accurate in human identification require multiple templates storage of the same user analogous to individual biometric sources. This may raises concern about their usage and security when these stored templates are compromised since each person is believed to have a unique biometric trait. Unlike passwords, the biometric templates cannot be revoked and switch to another set of uncompromised identifiers when compromised. Therefore, fool-proof techniques satisfying the requirements of diversity, revocability, security and performance are required to protect stored templates such that both the security of the application and the users2019; privacy are not compromised by the impostor attacks. Thus, this paper proposes a template protection scheme coined as random rectangular hashing to strengthen the multimodal biometric system. The performance of the proposed template protection scheme is measured using the fingerprint FVC2004 and PolyU palmprint database

An Overview on Privacy Preserving Biometrics

The Internet has consolidated itself as a very powerful platform that has changed the communication and business way. Nowadays, the number of users navigating through Internet is about 1,552 millions according to Internet World Stats. This large audience demands online commerce, e-government, knowledge sharing, social networks, online gaming . . . which grew exponentially over the past few years. The security of these transactions is very important considering the number of information that could be intercepted by an attacker. Within this context, authentication is one of the most important challenges in computer security. Indeed, the authentication step is often considered as the weakest link in the security of electronic transactions. In general, the protection of the message content is achieved by using cryptographic protocols that are well known and established. The well-known ID/password is far the most used authentication method, it is widely spread despite its obvious lack of security. This is mainly due to its implementation ease and to its ergonomic feature: the users are used to this system, which enhances its acceptance and deployment. Many more sophisticated solutions exist in the state of the art to secure logical access control (one time passwords tokens, certificates . . . ) but none of them are used by a large community of users for a lack of simplicity usage (O'Gorman, 2003)..